Pathology detection or classification in medical imaging using deep learning [1]}, [2]} continues to be an open research challenge. Although the field of computer vision has progressed significantly owing to advances in model architecture, optimization techniques, and data augmentation, learning relevant feature representations from visual data still remains a challenge [3]} [4]}.
The field of medical imaging using deep learning suffers from the same issues, since they rely mostly on the same deep learning approaches inspired by studies on general purpose datasets, like ImageNet [5]}. The issue becomes more challenging when test data is subject to distribution shifts [6]}.
When confronted with chest X-rays from different datasets, a straightforward approach, followed by [3]}, is to
merge these datasets and
form mini-batches by sampling uniformly from the merged dataset.
